Applicability of transfer functions for relative sea-level reconstructions in the southern North Sea coastal region based on salt-marsh foraminifera

We studied the foraminiferal distribution in two naturally grown salt marshes from the southern North Sea with respect to the tidal frame, salinity, grain size and pH. The salt marshes are situated on the landward sides of the islands of Sylt (Rantum, Germany) and Fano (Sonderho, Denmark). In both study areas, foraminifera have a vertical distribution with respect to water level, but also show inter-site variability, which can be related to environmental differences (e.g., in salinity and pH) and different flooding dynamics of the coastal salt marsh (Rantum) and the tidal-creek salt marsh (Sonderho). We developed different transfer functions, based on the widely applied standardized water level index (SWLI) approach and on three flooding parameters (duration of submergence (DoS), mean submergence time (MST), and flooding frequency (FF)), in order to assess their predictive ability for relative sea-level estimates for the southern North Sea coastal region. The water-level data used for these approaches are determined based on local water-level conditions, corrected for tidal distortions using water-level measurements for the Sonderho salt marsh and the Rantum tide gauge. The SWLI approach shows a precision (root mean squared error of prediction (RMSEP) of 0.23 m), which is around 15% of the tidal range. All three flooding approaches show comparable results at around mean tide level to mean high water, while at higher elevations, foraminiferal distribution becomes non-linearly correlated to flooding parameters resulting in lower precision of > 1.0 m. Our results suggest that the SWLI approach performs well and that the flooding approaches offer a suitable addition for assessing relative sea-level estimates in the North Sea region. We enhanced the knowledge on changing precision for tide level reconstructions along the elevational gradient in a storm dominated tidal area where elevation and flooding parameters are non-linear correlated.